The invention relates generally to image processing, and more particularly, to methods and systems for improving image quality by compounding a plurality of images to mitigate the effects of image noise.
Almost every kind of data contains noise. Noise reduction is a required step for any sophisticated algorithm used in image processing. In medical image processing applications such as X-ray, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT) and others, noise manifest during image capture, enhancement, and restoration needs to be suppressed while the original image structure is preserved. In medical imaging, image enhancement is important because it allows physicians to obtain a better visual interpretation, especially when viewing small structures (i.e. thin vessels). Moreover, enhancement is a preprocessing step for subsequent medical analysis, such as anatomy segmentation or registration of images from different modalities.
Most medical images have image quality characteristics such as contrast, sensitivity, detail (blurring), visual noise, spatial characteristics, and artifacts. Noise present in digital imaging is primarily due to the quantum noise inherent in photon detection and electronic noise. Electronic noise is relatively constant. However, quantum noise is related to the number of photons counted. Noise in an image is an undesirable, relatively high detail characteristic. Therefore, when images are processed to increase or enhance detail, the processing also increases the visibility of the noise. When reconstructing images from data, algorithms used employ filters that impact noise in the final image. Digital image processing generally reduces noise by blurring the image creating an undesirable tradeoff.
Using CT as one example, there are several adjustable protocol factors that have an effect on the image noise. Reducing voxel size, which increases detail, also increases noise because fewer protons are absorbed or captured in each voxel. Noise can be decreased by increasing the tube current—time product (mAs). However, this increases the dose to the patient.
Noise is produced by the random variation or difference in the number of photons from one voxel to another. The statistical variation in image noise increases as the number of photons, exposure, and dose, is decreased. Small voxels, as used for better detail, capture less photons and result in more noise.
For example, if a CT slice thickness is decreased to improve image detail, the noise level will increase because of the smaller voxels. If the mAs is then increased to maintain the same noise level, the radiation dose will be increased. This is why thin CT slices are only used when necessary from a clinical perspective.
Filter algorithms can either decrease or increase noise content depending on what type is selected. Filtering is a preliminary process in many medical image processing applications. It is a fundamental operation in low level computer vision, aiming at restoring a noise-corrupted image to its noiseless counterpart. Any post-processing tasks such as segmentation and feature enhancement benefit from noise reduction.
Extensive research has been conducted to improve image quality based on a single image. Usually, the noise on neighboring pixels is independent and hence can be easily reduced by a low-pass filter. But this filter also blurs the sharp edges in the image. To preserve the high frequency signal in the image, e.g. edges or corners, prior knowledge has to be used to discriminate the signal from the noise. Various methods are proposed to model the high frequency signal in images, e.g. edge modeling based on Markov Random Field or quadratic signal class. The difficulties arise from the fact that accurate modeling of various signal and noise is usually very hard if not impossible.
Multiple images can be obtained in some cases to further improve imaging quality, which is also called image compounding. For example, in ultrasound B-scans, multiple images of the same tissue using different frequency ultrasound can be generated. The speckle noises on different images are proved to be independent. This is different from the traditional multiple-image based super-resolution techniques. In image compounding, the images have the same underlying signal but are corrupted by independent noise. The multiple images do not provide more information for super-resolution but help reducing noise. This topic is much less studied than the single image based restoration methods and is usually handled with very simple weighted averaging, followed by the traditional restoration methods on the averaged image. But these schemes fail to fully utilize the abundant information hinted by the multiple images and cannot significantly improve the image quality with only a small number of images, e.g., 2 or 3 images.
While various techniques exist for image noise reduction, an ideal filtering technique has yet to be introduced. Achieving image noise reduction has proven problematic most often due to new problems arising while undergoing the filtering to reduce noise content. What is desired is a method and system for removing existing noise content and obviating the introduction of new noise content during image reconstruction.